Method and apparatus for autonomous tool parameter impact identification system for semiconductor manufacturing

ABSTRACT

A system and method for autonomously determining the impact of respective tool parameters on tool performance in a semiconductor manufacturing system is provided. A parameter impact identification system receives tool parameter and tool performance data for one or more process runs of the semiconductor fabrication system and generates a separate function for each tool parameter characterizing the behavior of a tool performance indicator in terms of a single one of the tool parameters. Each function is then scored according to how well the function predicts the actual behavior of the tool performance indicator, or based on a determined sensitivity of the tool performance indicator to changes in the single tool parameter. The tool parameters are then ranked based on these scores, and a reduced set of critical tool parameters is derived based on the ranking. The tool performance indicator can then be modeled based on this reduced set of tool parameters.

TECHNICAL FIELD

The subject invention relates generally to techniques for determiningrelative impacts of tool parameters on selected tool performanceindicators of a semiconductor manufacturing system.

BACKGROUND

Progressive technological evolution of electronics and computing devicesmotivates advances in semiconductor technology. Growing consumer demandfor smaller, higher performance, and more efficient computer devices andelectronics has led to down-scaling of semiconductor devices. To meetdevice demand while restraining costs, silicon wafers upon whichsemiconductor devices are formed have increased in size.

Fabrication plants working with large wafer sizes utilize automation toimplement and control wafer processing. Such plants can be capitalintensive, and accordingly, it is desirable to maintain highly efficientoperation of fabrication equipment to minimize downtime and maximizeyields. To facilitate these goals, measurement equipment is oftenemployed to monitor fabrication equipment during wafer processing and toacquire measurement information on both the equipment and the processedwafer. The measurement information can then be analyzed to optimizefabrication equipment.

According to an example, measurement information can include tool levelinformation, which indicates a state or condition of fabricationequipment or a portion thereof, wafer metrology information specifyingphysical and/or geometric conditions of wafers being processed,electrical text information, and the like. In addition, spectroscopicdata (e.g., spectral line intensity information), can be gathered tofacilitate identification of etch endpoints by process engineers.However, in conventional fabrication environments, various measurementdata is handled independently of one another for different purposes. Assuch, inter-relationships among various measurement data are notleveraged for advanced optimization of fabrication processes.

The above-described deficiencies of today's semiconductor fabricationmeasurement and optimization systems are merely intended to provide anoverview of some of the problems of conventional systems, and are notintended to be exhaustive. Other problems with conventional systems andcorresponding benefits of the various non-limiting embodiments describedherein may become further apparent upon review of the followingdescription.

SUMMARY

The following presents a simplified summary in order to provide a basicand general understanding of some aspects of exemplary, non-limitingembodiments described herein. This summary is not an extensive overviewnor is intended to identify key/critical elements or to delineate thescope of the various aspects described herein. Instead, the sole purposeof this summary is to present some concepts in a simplified form as aprelude to the more detailed description of various embodiments thatfollow.

One or more embodiments of the present disclosure relate to techniquesfor autonomously identifying relative impacts of tool parameter onselected tool performance indicators of a semiconductor fabricationsystem. To this end, a parameter impact identification system isprovided that can leverage measured tool parameter data and toolperformance data to identify the most critical tool parameters thatinfluence a particular tool performance metric. The parameter impactidentification system can also rank these critical parameters in orderof their relative impact on the selected tool performance metric,providing maintenance personnel with a useful guide for identifyingwhich critical tool parameters should be the focus of maintenanceefforts to optimize the selected tool performance metric.

The parameter impact identification system can determine the relativeimpacts of the respective tool parameters by separately analyzing eachtool parameter and, for each parameter, attempting to predict thebehavior of a selected tool performance indicator using the separatedparameter. This reduces the complex dimensionality of semiconductor toolparameters into a single-input-single-output (SISO) problem, in whichthe tool performance indicator is described as a function of only asingle tool parameter. The impact of each parameter on the performanceindicator can then be determined based on an analysis of the resultingfunctions (e.g., by calculating a derivative of each function, bydetermining a predictive accuracy at each function, etc.), and the toolparameters ranked according to relative impact.

In some embodiments, the parameter impact identification system can alsogenerate a function that characterizes the selected tool performanceindicator in terms of only the most important tool parameters asdetermined by the aforementioned ranking. By eliminating tool parametershaving negligible impact on the performance indicator, the resultingfunction can greatly simplify the problem space for the end user andallow sharper focus on the critical tool parameters.

To the accomplishment of the foregoing and related ends, certainillustrative aspects are described herein in connection with thefollowing description and the annexed drawings. These aspects areindicative of various ways which can be practiced, all of which areintended to be covered herein. Other advantages and novel features maybecome apparent from the following detailed description when consideredin conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is block diagram illustrating an exemplary system for collectingand analyzing information relating to semiconductor production.

FIG. 2 is a block diagram of an exemplary parameter impactidentification system that can autonomously identify tool parametersthat impact selected tool performance metrics.

FIG. 3 is a block diagram illustrating processing functions performed byan exemplary parameter impact identification system.

FIG. 4 illustrates an exemplary interface for selecting a toolperformance indicator to be analyzed.

FIG. 5 illustrates an exemplary interface for selecting one or more toolparameters that are to be considered by parameter impact identificationsystem.

FIG. 6 illustrates generation of a set of isolated parameter functionsgiven a set of tool parameter and performance data.

FIG. 7 illustrates assignment of quality scores to respective toolparameters based on isolated parameter functions.

FIG. 8 illustrates assignment of sensitivity scores to respective toolparameters based on isolated parameter functions.

FIG. 9 illustrates ranking of tool parameters according to relativeimpact on a tool performance indicator.

FIG. 10 illustrates filtering of ranked tool parameters to identify aset of critical tool parameters having the highest impact on a toolperformance indicator.

FIG. 11 illustrates an exemplary interface for configuring toolparameter filtering criteria.

FIG. 12 graphically summarizes identification of critical toolparameters according to one or more embodiments of the parameter impactidentification system.

FIG. 13 illustrates generation of a composite function thatcharacterizes a tool performance behavior in terms of a reduced set ofcritical tool parameters.

FIG. 14 illustrates updating of tool performance function in acontinuously iterative manner.

FIG. 15 is a flowchart of an example methodology for modeling afunctional relationship between a tool performance indicator and a setof tool parameters of a semiconductor fabrication system.

FIG. 16 is a flowchart of an example methodology for autonomouslyidentifying and modeling tool parameter impact on a tool performancemetric.

FIG. 17 is an example computing environment.

FIG. 18 is an example networking environment.

DETAILED DESCRIPTION

The subject innovation is now described with reference to the drawings,wherein like reference numerals are used to refer to like elementsthroughout. In the following description, for purposes of explanation,numerous specific details are set forth in order to provide a thoroughunderstanding of the present invention. It may be evident, however, thatthe present invention may be practiced without these specific details.In other instances, well-known structures and devices are shown in blockdiagram form in order to facilitate describing the present innovation.

As used in the subject specification and drawings, the terms “object,”“module,” “interface,” “component,” “system,” “platform,” “engine,”“selector,” “manager,” “unit,” “store,” “network,” “generator” and thelike are intended to refer to a computer-related entity or an entityrelated to, or that is part of, an operational machine or apparatus witha specific functionality; such entities can be either hardware, acombination of hardware and firmware, firmware, a combination ofhardware and software, software, or software in execution. In addition,entity(ies) identified through the foregoing terms are hereingenerically referred to as “functional elements.” As an example, acomponent can be, but is not limited to being, a process running on aprocessor, a processor, an object, an executable, a thread of execution,a program, and/or a computer. By way of illustration, both anapplication running on a server and the server can be a component. Oneor more components may reside within a process and/or thread ofexecution and a component may be localized on one computer and/ordistributed between two or more computers. Also, these components canexecute from various computer-readable storage media having various datastructures stored thereon. The components may communicate via localand/or remote processes such as in accordance with a signal having oneor more data packets (e.g., data from one component interacting withanother component in a local system, distributed system, and/or across anetwork such as the Internet with other systems via the signal). As anexample, a component can be an apparatus with specific functionalityprovided by mechanical parts operated by electric or electroniccircuitry, which is operated by software, or firmware applicationexecuted by a processor, wherein the processor can be internal orexternal to the apparatus and executes at least a part of the softwareor firmware application. As another example, a component can be anapparatus that provides specific functionality through electroniccomponents without mechanical parts, the electronic components caninclude a processor therein to execute software or firmware that confersat least in part the functionality of the electronic components.Interface(s) can include input/output (I/O) components as well asassociated processor(s), application(s), or API (Application ProgramInterface) component(s). While examples presented hereinabove aredirected to a component, the exemplified features or aspects also applyto object, module, interface, system, platform, engine, selector,manager, unit, store, network, and the like.

Moreover, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or”. That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. In addition, the articles “a” and “an” as usedin this application and the appended claims should generally beconstrued to mean “one or more” unless specified otherwise or clear fromcontext to be directed to a singular form.

Furthermore, the term “set” as employed herein excludes the empty set;e.g., the set with no elements therein. Thus, a “set” in the subjectdisclosure includes one or more elements or entities. As anillustration, a set of components includes one or more components; a setof variables includes one or more variables; etc.

Various aspects or features will be presented in terms of systems thatmay include a number of devices, components, modules, and the like. Itis to be understood and appreciated that the various systems may includeadditional devices, components, modules, etc. and/or may not include allof the devices, components, modules etc. discussed in connection withthe figures. A combination of these approaches also can be used.

FIG. 1 is block diagram illustrating an exemplary system 100 forcollecting and analyzing information relating to semiconductorproduction. As shown in FIG. 1, a semiconductor fabrication system 110can receive input wafers 102 and output processed wafers 104. In anexemplary, non-limiting embodiment, semiconductor fabrication system 110can be an etch tool that removes unmasked material from input wafers 102via an etching process (e.g., wet etch, dry etch, plasma etching, etc.)to generate processed wafers 104 having cavities and features formedthereon. Semiconductor fabrication system 110 can also be a depositiontool (e.g., atomic layer deposition, chemical vapor deposition, etc.)that deposits material onto input wafers 102 to yield processed wafers104.

A variety of measurement devices, such as spectroscope 120, tool sensors130, and device measurement equipment 140, can monitor the processperformed by semiconductor fabrication system 110 to acquire disparateinformation relating to various aspects, conditions, or results of theprocess. As an example, spectroscope 120 can acquire spectral intensityinformation which includes a set of intensities for respectivewavelengths or spectral lines observable by spectroscope 120. Spectralintensity information can be time-series data such that spectroscope 120measures intensities for respective wavelengths at regular intervals(e.g., every second, every 2 seconds, every 100 milliseconds, etc.).Spectroscope 120 can also correlate spectral intensity information withwafer IDs associated with specific wafers processed by semiconductorfabrication system 110. Accordingly, spectroscope 120 can acquirespectral intensity information individually for each wafer processed bysemiconductor fabrication system 110.

Tool sensors 130 can monitor and measure tool operation characteristicswhile semiconductor fabrication system 110 processes input wafers 102and generate corresponding sensor information. Tool sensor information,similar to spectral intensity information measured by spectroscope 120,can be time-series data correlated on a per-wafer basis. Tool sensorinformation can include measurements from a variety of sensors. Suchmeasurements can include, but are not limited to, pressures within oneor more chambers of semiconductor fabrication system 110, gas flows forone or more distinct gases, temperatures, upper radio frequency (RF)power, elapsed time since last wet-clean, age of tool parts, and thelike.

Device measurement equipment 140 can measure physical and geometricproperties of wafers and/or features fabricated on wafers. For instance,device measurement equipment 140 can measure development inspectioncritical dimension (DI-CD), final inspection critical dimension (FI-CD),etch bias, thickness, and so forth, at predetermined locations orregions of wafers. The measured properties can be aggregated on aper-location, per-wafer basis and output as device measurementinformation. Properties of wafers are typically measured beforeprocessing or after processing. Accordingly, device measurementinformation is typically time-series data acquired at a differentinterval as compared with spectral intensity information and tool sensorinformation.

By leveraging data collected from measurement devices 120, 130, and 140during wafer production, a large number of tool performance indicatorscan be measured or derived for semiconductor fabrication system 110.Exemplary performance indicators can include overall productionstatistics, such as wafer throughput, system downtime, system uptime,etc. Performance indicators can also include metrology outputs, ormeasured characteristics of the resulting semiconductor wafers producedby the system, including but not limited to edge bias, depositionthickness, particle count (e.g., level of contamination), sidewallangle, or other such measured characteristics. Some performanceindicators can also consider data external to the semiconductorfabrication system 110. For example, repair costs associated with thesystem can be calculated based in part on recorded financial or billingdata obtained from one or more business-level server.

Behavior of a given tool performance indicator is typically a functionof one or more tool parameters measured for the system. Tool parametershaving an impact on tool performance can include, for example, chamberpressures, temperatures, RF power, gas flows, or other parametersmeasured during operation of the tools. Other tool parameters that canaffect tool performance include tool operational characteristics, suchas age of parts, processing times, tool set-up times, wafer load orunload times, etc. Some tool performance indicators can also be afunction of one or more product metrology inputs (e.g., incomingcritical dimension, deposited thickness, refractive index of material,or other such metrology values).

Since tool performance is largely a function of one or more toolparameters such as those described above, knowledge of which toolparameters have the largest impact on a given tool performance metricwould afford users a greater degree of control over tool performance andhelp to maintain tool performance indicators within desired limits.However, since tool parameter data is often only loosely correlated withtool performance data, it is difficult to assess which tool parametershave the highest impact on these tool performance metrics. Thisinformation would be useful to equipment owners in connection withidentifying where maintenance efforts should be focused.

To address these issues, a parameter impact identification system 160 isprovided that leverages tool parameter data and tool performance data toautonomously identify which tool parameters have the largest impact on aselected tool performance indicator. Parameter impact identificationsystem 160 can also characterize tool performance indicators in terms ofa reduced number of tool parameters determined to have the mostinfluence on the performance indicator, thereby simplifying analysis byreducing dimensional complexity.

Parameter impact identification system 160 can receive, as input, toolparameter data 108 and tool performance data 112. In one or moreembodiments, this input data can be derived from tool process logs thatrecord parameter and performance data measured during respective runs ofsemiconductor fabrication system 110. Tool process logs can includemeasurement data from one or more of the spectroscope 120, tool sensors130, or device measurement equipment 140. Measurements recorded in suchtool process logs can include, but are not limited to, sensor readings(e.g., pressures, temperatures, power, etc.), maintenance relatedreadings (e.g., age of focus ring, age of mass flow controller, timesince last performed maintenance, time since last batch of resist wasloaded, etc.), and/or tool and performance statistics (e.g., time toprocess wafer, chemical consumption, gas consumption, etc.).

In an exemplary scenario, a tool process log can be generated by areporting component 150 at the end of each process run of semiconductorfabrication system 110. At the end of a process run, data from one ormore of the spectroscope 120, tool sensors 130, or device measurementequipment 140 can be provided to reporting component 150, whichaggregates the collected data in a tool process log for the run. A toolprocess log can correspond to a single semiconductor wafer processedduring the run, or a batch of semiconductors fabricated during the run.The tool process logs can then be stored for reporting or archivalpurposes. Tool parameter data 108 and tool performance data 112 from thetool process logs can be provided to parameter impact identificationsystem 160 either manually by an operator or automatically by reportingcomponent 150 or a related device.

Although the foregoing example describes tool parameter data 108 andtool performance data 112 as being retrieved or extracted from toolprocess logs, it is to be appreciated that this data may also beprovided to parameter impact identification system 160 by other means.For example, in some embodiments, all or a subset of tool parameter data108 or tool performance data 112 may be provided directly to parameterimpact identification system 160 from devices 120, 130, or 140.

Tool parameter data 108 can comprise values measured for one or moretools during operation (e.g., pressures, temperatures, power, gas flows,etc.), operational performance statistics (e.g., part age or usagecount, processing times, set-up times, load or unload times, etc.), orother such tool parameters. Tool performance data 112 can includemeasured characteristics of the finished semiconductor wafers which areimpacted by one or more of the tool parameters (e.g., etch bias,deposition thickness, particle count, sidewall angle, etc.), performancedata for the tool itself (e.g., wafer throughput, downtime, uptime,repair costs, etc.), or other such metrics indicative of the tool'soperational performance.

Parameter impact identification system 160 processes tool parameter data108 and tool performance data 112 in view of user specifications 106defined by the user. User specifications 106 can specify one or moreprocessing preferences, including but not limited to selection of whichtool performance indicator is to be analyzed, which tool parameters areto be considered (e.g., which tool parameters are to be correlated withthe selected tool performance indicator), a preferred number of top toolparameters to be identified by the system, a preferred learningmethodology (e.g., simulated annealing, symbolic regression, etc.), orother user preferences. Parameter impact identification system 160analyzes tool parameter data 108 and tool performance data 112 in viewof user specifications 106 to generate analysis results 154, to bedescribed in more detail below. In general, analysis results 154 assistin identifying critical tool parameters having the greatest impact on agiven tool performance indicator, characterizing the selected toolperformance indicator as a function of the critical tool parameters, andpredicting future values of the tool performance indicator given theidentified tool parameters.

FIG. 2 is a block diagram of an exemplary parameter impactidentification system that can autonomously identify tool parametersthat impact selected tool performance behaviors. Aspects of the systems,apparatuses, or processes explained in this disclosure can constitutemachine-executable components embodied within machine(s), e.g., embodiedin one or more computer-readable mediums (or media) associated with oneor more machines. Such components, when executed by one or moremachines, e.g., computer(s), computing device(s), automation device(s),virtual machine(s), etc., can cause the machine(s) to perform theoperations described.

Parameter impact identification system 202 can include an interfacecomponent 204, a parameter separation component 206, a quality scoringcomponent 208, a sensitivity component 210, a ranking component 212, afiltering component 214, a composite function component 216, one or moreprocessors 218, and memory 220. In various embodiments, one or more ofthe interface component 204, parameter separation component 206, qualityscoring component 208, sensitivity component 210, ranking component 212,filtering component 214, composite function component 216, one or moreprocessors 218, and memory 220 can be electrically and/orcommunicatively coupled to one another to perform one or more of thefunctions of the parameter impact identification system 202. In someembodiments, components 204, 206, 208, 210, 212, 214, and 216 cancomprise software instructions stored on memory 220 and executed byprocessor(s) 218. The parameter impact identification system 202 mayalso interact with other hardware and/or software components notdepicted in FIG. 2. For example, processor(s) 218 may interact with oneor more external user interface devices, such as a keyboard, a mouse, adisplay monitor, a touchscreen, or other such interface devices.

Interface component 204 can be configured to receive input from andprovide output to a user of parameter impact identification system 202.For example, interface component 204 can render an input display screento a user that prompts for user specifications, and accepts suchspecifications from the user via any suitable input mechanism (e.g.,keyboard, touch screen, etc.). Parameter separation component 206 can beconfigured to generate functions that isolate the effects of eachindividual tool parameter to determine the impact of each tool parameteron a selected tool performance indicator. Each function attempts topredict the behavior of the selected tool performance indicator as afunction of a single tool parameter. Quality scoring component 208 canbe configured to score each tool parameter according to how well theparameter's function predicts the actual behavior of the toolperformance indicator. Sensitivity component 210 can be configured todetermine a sensitivity of the selected tool performance indicator toeach tool parameter based in part on the functions generated byparameter separation component 206. Ranking component 212 can beconfigured to rank the tool parameters according to their respectiveimpact on the tool performance indicator being analyzed (e.g., asdetermined by quality scoring component 208 or sensitivity component210). Filtering component 214 can be configured to eliminate fromconsideration one or more tool parameters determined to have the leastimpact on the tool performance indicator. Composite function component216 can be configured to generate a function describing the toolperformance indicator as a function of the reduced set of toolparameters after filtering component 214 has eliminated the leastinfluential parameters. The one or more processors 218 can perform oneor more of the functions described herein with reference to the systemsand/or methods disclosed. Memory 220 can be a computer-readable storagemedium storing computer-executable instructions and/or information forperforming the functions described herein with reference to the systemsand/or methods disclosed.

FIG. 3 is a block diagram illustrating processing functions performed byan exemplary parameter impact identification system. As described abovein connection with FIG. 1, parameter impact identification system 308receives, as input, tool parameter data 304 and tool performance data306 associated with one or more runs of semiconductor fabrication system302. Tool parameter data 304 and tool performance data 306 can beprovided automatically to parameter impact identification system 308(e.g., by reporting component 150 of FIG. 1) or provided to the systemmanually by a user via interface component 324.

In addition to tool parameter data 304 and tool performance data 306,parameter impact identification system 308 also receives userspecifications 312 from a user via interface component 324. As notedabove, user specifications 312 can specify which tool performanceindicator is to be analyzed, which tool parameters are to be consideredin terms of their respective impact on the selected tool performanceindicator, a selected number of top tool parameters to be identified bythe system (or alternatively, a selected number of least influentialtool parameters to be identified and eliminated), a preferred learningmethodology (e.g., simulated annealing, symbolic regression, etc.), orother user preferences.

Exemplary non-limiting interfaces for defining one or more userspecifications are described in connection with FIGS. 4 and 5. Exemplaryinterfaces 400 and 500 can be rendered to a user via interface component324. Exemplary interface 400 of FIG. 4 can be used to select a toolperformance indicator to be analyzed by the system. Exemplary interface500 of FIG. 5 can be used to select one or more tool parameters that areto be considered by parameter impact identification system 308. That is,parameter impact identification system 308 will assess the relativeimpacts of the tool parameters selected via exemplary interface 500 onthe tool performance indicator selected via exemplary interface 400.Exemplary interfaces 400 and 500 allow tool performance indicators andtool parameters to be selected using checkboxes; however, any suitabletechnique for entering tool performance and tool parameterspecifications can be employed and are within the scope of one or moreembodiments of this disclosure.

Returning now to FIG. 3, processing operations performed on toolparameter data 304, tool performance data 306, and user specifications312 are described. After tool parameter data 304, tool performance data306, and user specifications 312 are provided to parameter impactidentification system 308, parameter separation component 310 separateseach tool parameter (e.g., each tool parameter selected for analysis viauser specifications 312) and iteratively attempts to predict thebehavior of the selected tool performance indicator using each separatedtool parameter individually. This process is described in more detailwith reference to FIG. 6. In the present example, tool parameters P0-PNare to be considered, where N is an integer greater than zero.Accordingly, tool parameter data 304 comprises parameter data measuredfor each of the tool parameters P0-PN for one or more process runs ofsemiconductor fabrication system 302. Tool performance data 306comprises corresponding measurement data for a selected tool performanceindicator for the same one or more process runs.

Parameter separation component 310 leverages tool parameter data 304 andtool performance data 306 to generate a set of isolated parameterfunctions 602, where each function corresponds to a single one of thetool parameters P0-PN. Each of the isolated parameter functions 602characterizes the predicted behavior of the tool performance indicator(represented as output O) in terms of a single tool parameter. Theresulting isolated parameter functions corresponding to tool parametersP0-PN can be represented as follows:

$\begin{matrix}{O = {f_{0}\left( {P\; 0} \right)}} & (1) \\{{O = {f_{1}\left( {P\; 1} \right)}}\ldots} & (2) \\{O = {f_{N - 1}\left( {{PN} - 1} \right)}} & (3) \\{O = {f_{N}({PN})}} & (4)\end{matrix}$

These functions establish non-linear functional relationships betweenthe selected tool performance indicator (output O) and each toolparameter P0-PN. For the equations above, the tool performance indicatoroutput O will be the same for each function, but will be described bydifferent functions f₀-f_(N) which are, respectively, functions of thetool parameters P0-PN. Thus, parameter separation component 310 reducesthe complex dimensionality of the semiconductor tool parameters into aset of single-input-single-output (SISO) sub-problems.

Parameter separation component 310 can utilize any suitable learningmethod to learn the non-linear functional relationships f for each ofthe parameters P0-PN, including but not limited to genetic programming,symbolic regression, simulated annealing, neural network, or other suchnon-linear functional identification systems. In one or moreembodiments, users may choose a preferred learning method to be used byparameter separation component 310 to derive functions f₀(P0) . . .f_(N)(PN). In such embodiments, selection of a preferred learning methodcan be made via interface component 324. Parameter separation component310 can be also configured to iteratively update functions f₀(P0) . . .f_(N)(PN) as new tool parameter data 304 and/or tool performance data306 is received.

Returning now to FIG. 3, after parameter separation component 310 hasestablished the functional relationships f₀(P0) . . . f_(N)(PN)describing the function relationships between each tool parameter andthe tool performance indicator, the resulting functions can be submittedfor critical parameter identification. To facilitate identification ofthe critical tool parameters having the greatest impact on the selectedtool performance indicator, parameter impact identification system 308can utilize one or more of a quality scoring component 326, asensitivity component 314, a filtering component 316, and a rankingcomponent 318.

Quality scoring component 326 is described in more detail with referenceto FIG. 7. Quality scoring component 326 can determine a quality scorefor each of the isolated parameter functions f₀(P0) . . . f_(N)(PN) bycomparing a predicted output O of each function with actual toolperformance data 306. The resulting set of quality scores 702 indicatehow well the tool performance behavior predicted by each toolparameter's isolated function matches the actual tool performance data306. For example, when new tool parameter data 304 for parameters P0-PNis received after a new process run of semiconductor fabrication system302, quality scoring component 326 can run the new value of parameter P0through that parameter's corresponding isolated function ƒ₀(P0) todetermine the value of the tool performance indicator predicted by theparameter's isolated function (the value O). Quality scoring component326 can then compare this predicted value O with the actual valueindicated by tool performance data 306 and assign a quality score toparameter P0 indicating how closely predicted value O matches the actualvalue of the tool performance indicator. Quality scoring component 326repeats this scoring process for each of the remaining parameters P1-PNto derive a set of quality scores 702. In one or more embodiments,quality scoring component 326 can update the quality scores 702 in aniterative fashion as new process run data is received.

Quality scores 702 can provide a metric for determining relative degreesof impact each parameter P0-PN has on the tool performance indicatorbeing analyzed. In general, a tool parameter whose isolated parameterfunction predicts a tool performance output O that closely matches theactual tool performance indicator is likely to have a relatively highdegree of influence on the tool performance indicator. Conversely, atool parameter whose isolated function repeatedly fails to closelypredict the actual tool performance is less likely to have a correlationwith the tool parameter indicator. Accordingly, quality scores 702reflect these relative degrees of impact.

Other techniques may be used to determine relative impacts of the toolparameters on the tool performance indicator. For example, someembodiments of parameter impact identification system 308 may include asensitivity component 314 in addition to or as an alternative to qualityscoring component 326. As illustrated in FIG. 8, sensitivity component314 can assess each isolated parameter functions 602 and assignsensitivity scores 802 to the respective tool parameters P0-PN based onthe assessment. Similar to quality scores 702, sensitivity scores 802indicate relative degrees of impact that each tool parameter has on thetool performance indicator being analyzed. In general, a sensitivityscore for a given parameter is a measure of how sensitive the toolperformance indicator is to changes in that tool parameter.

In one or more embodiments, sensitivity component 314 can generate thesensitivity scores 802 based in part on numerical or symbolicdifferentials computed for each isolated parameter function ƒ₀(P0) . . .f_(N)(PN). For example, for a given isolated parameter functionO=fi(Pi)corresponding to a tool parameter Pi, sensitivity component 314can use numerical or symbolic differentiation to calculate adifferential:

$\begin{matrix}\frac{f_{i}}{P_{i}} & (5)\end{matrix}$

This differential represents the rate at which the predicted value ofthe tool performance indicator O changes in response to changes in thetool parameter Pi, and is a measure of how sensitive the tool parameterindicator is to changes in tool parameter Pi. Sensitivity component 314can generate sensitivity scores 802 based in part on respectivedifferentials computed for each tool parameter P0-PN.

Since parameters P0-PN will typically represent different types of toolparameters represented by different engineering units and havingdifferent operational ranges, it may be necessary for sensitivitycomponent 314 to normalize the differentials in some manner in order toaccurately compare the respective sensitivities. Sensitivity component314 may also consider each tool parameter's valid operating range whenderiving a sensitivity score based on the differential. For example, ifa given tool parameter has known upper and lower operating limits,sensitivity component 314 may consider only the portion of theparameter's differential curve between these two operating limits whencomputing the parameter's sensitivity score. In general, any suitabletechnique or methodology for deriving a sensitivity score for a toolparameter based on a differential of the parameter's isolated parameterfunction is within the scope of one or more embodiments of thisdisclosure.

One or more embodiments of parameter impact identification system 308may score each parameter P0-PN using only one of the quality scoringcomponent 326 or the sensitivity component 314. Other embodiments mayinclude both the quality scoring component 326 and the sensitivitycomponent, and generate scores for each parameter based on a compositeof the quality score and the sensitivity score. In the latter scenario,parameter impact identification system 308 can combine the quality andsensitivity scores using any suitable combining technique. For example,parameter impact identification system 308 may apply a weighing factorto a tool parameter's quality score based on the parameter's sensitivityscore (or vice versa). In another example, the two scores may he addedtogether. These combining techniques are only intended to be exemplary,and any suitable technique for generating a composite score based on thequality and sensitivity scores is within the scope of one or moreembodiments of this disclosure.

Once a set of parameter scores has been obtained, ranking component 318can rank the tool parameters based on these scores. FIG. 9 illustratesan exemplary parameter ranking performed by ranking component 318.Parameter scores 902 for each tool parameter P0-PN can comprise thequality scores, the sensitivity scores, or a composite of both scores asdescribed above. These scores represent relative impact or influenceeach tool parameter has on the tool performance indicator beinganalyzed. Based on these scores 902, ranking component 318 ranksparameters P0-PN in order of their impact on the tool performanceindicator.

The resulting tool parameter ranking 904 can be used to identify a firstsubset of higher-ranked tool parameters 906 having a significant impacton the tool performance indicator, and a second subset of lower-rankedtool parameters 908 having a negligible or non-existent impact on thetool performance indicator. Based on this determination, parameterimpact identification system 308 can reduce the dimensional complexityof tool performance indicator analysis by eliminating the lower-rankedsubset of tool parameters 908 whose effect on the tool performanceindicator is negligible. Accordingly, filtering component 316 canreceive the ranked tool parameters 904 generated by ranking component318 and eliminate the subset of tool parameters 908 from consideration,as illustrated in FIG. 10. The remaining set of top tool parameters 1002represent those tool parameters identified as having a non-trivialimpact on the tool parameter indicator.

One or more embodiments of parameter impact identification system 308can allow the user to specify a number of bottom-ranking tool parametersto eliminate, or a number of top-ranking tool parameters to maintain.This affords the user control over the degree of dimensional complexityof subsequent analysis. FIG. 11 illustrates an exemplary user interface1100 for making this selection. Exemplary user interface 1100 caninclude selectable data entry fields allowing the user to specify anumber of top parameters to keep (data field 1102) or a number of bottomparameters to eliminate (data field 1104). Filtering component 316 canuse these configuration selections to filter ranked tool parameters 904accordingly and output the list of top tool parameters 1002.

FIG. 12 summarizes the tool parameter identification processingperformed by parameter impact identification system 308. For each toolparameter P0-PN being considered, an isolated parameter function isgenerated that defines a non-linear functional relationship between thetool parameter and a tool performance indicator being analyzed. Thesefunctions are represented as f₀(P0) . . . f_(N)(PN). Output O representsthe predicted value of the selected tool performance indicator as afunction of only a single tool parameter. For example, an output O of afunction corresponding to parameter Pi represents a predicted value ofthe tool performance indicator as a function of only tool parameter Pi.

Functions f₀(P0) . . . f_(N)(PN) are then scored based on the determineddegree of impact or influence the corresponding tool parameters have onthe tool performance indicator. Scores can be generated based on howwell the respective output O matches the actual measured values for thetool performance indicator, a numeric or symbolic differentialcalculated for the respective functions f₀(P0) . . . f_(N)(PN), or acombination of these techniques. Parameters P0-PN are then ranked basedon these scores. Top-ranking parameters 906 can then be identified andmaintained for further analysis, while bottom-ranking parameters 908(representing tool parameters having a trivial or non-existent impact onthe tool performance parameter) can be eliminated from consideration.

Once the top ranking tool parameters are identified, parameter impactidentification system 308 can render these results to a user (e.g., viainterface component 324). By providing users with a list of toolparameters determined to have the greatest influence on the toolperformance indicator, parameter impact identification system 308 canprovide guidance as to where maintenance efforts should be focused inorder to keep the tool performance indicator within desired operatinglimits.

In one or more embodiments, parameter impact identification system 308may perform further analysis on the top-ranked tool parameters toprovide additional insight into the relationships between the toolparameters and tool performance indicators. In particular, once thenumber of tool parameters P0-PN has been reduced to an identified subsetof important parameters, output O can be re-learned by the system as afunction of this reduced set of tool parameters. To this end, the systemmay include a composite function component 320 configured to generate anew function that characterizes the tool performance indicator output(O_(NEW)) as a function of the most important tool parameters identifiedby filtering component 316. As illustrated in FIG. 13, compositefunction component 320 receives top tool parameters 1002 identified byfiltering component 316 as having the greatest impact on the toolperformance indicator, and learns a new composite function 322 thatpredicts tool impact parameter output O_(NEW) as a function of the toptool parameters 1002. For example, for a set of top-ranked toolparameters P1, P8, P0 . . . composite function component 320 cangenerate a composite function 322 having the following general format:

O _(NEW) =f(P1,P8,P0 . . . )  (6)

Composite function component 320 can employ any suitable non-linearfunctional identification technique to learn the composite function,including but not limited to genetic programming, symbolic regression,neural networks, least squares fit, or other suitable techniques.

Composite function 322 greatly simplifies analysis of the toolperformance indicator by reducing the problem space to a relativelysmall set of critical tool parameters, allowing users to focus moresharply on those critical parameters. The composite function can beleveraged in a number of ways to facilitate analysis of a selected toolperformance aspect with respect to the tool parameters that determinethe behavior of this performance aspect. For example, new tool parameterdata can be analyzed in view of composite function 322 in order topredict future values of the tool performance indicator. If one or moretool parameter values begin drifting due to part degradation, expectedfuture values of these tool parameters can be run through compositefunction 322 to determine when the tool performance indicator O_(NEW) isexpected to fall outside acceptable performance limits. In this way,composite function 322 can be used as a basis for a near real-time earlywarning system that identifies when preventative maintenance should beperformed and which tool parameters should be the focus of maintenanceefforts. Composite function 322 can also be analyzed more generally toprovide insight into the relationships between the critical toolparameters and the predicted tool performance indicator O_(NEW). Thus,parameter impact identification system 308 serves as an efficientfunctional modeling system that reduces the search space for performingfunctional relationship modeling for a semiconductor fabrication system.

In some scenarios, the parameter impact identification system 308 mayperform a one-time, on-demand calculation of composite function 322and/or tool parameter rankings for a given set of tool process logsprovided to the system (e.g., a set of tool run data provided to thesystem by a user). However, some embodiments of the parameter impactidentification 308 system may also be configured to operate in acontinuous iterative manner as new tool data is collected on asubstantially real-time basis. This iterative processing is illustratedin FIG. 14. Parameter impact identification system 308 may be configuredto receive tool parameter data 304 and tool performance data 306directly from a semiconductor fabrication system as the new data becomesavailable (e.g., at the end of each tool run), and iteratively updatecomposite function 322 in view of user specifications 312 based on thenew data. Composite function 322 can be maintained in a data store 1402and used as a basis for a continuously updated model for predictingfuture tool performance, identifying critical tool parameters affectingtool performance, etc. In addition to recalculating composite function322, parameter impact identification system 308 can also re-estimate thecritical tool parameter rankings as new tool data is received. In thisway, parameter sensitivity to tool drift, tool maintenance, and otherwear and tear is factored into consideration by virtue of the continuousiterative nature of the individual tool parameter learning.

The parameter impact identification system described herein canautonomously identify and model correlations between tool parameters andtool performance indicators regardless of tool complexity. This isachieved by reducing a large set of potentially relevant tool parametersto a smaller set of critical tool parameters and modeling relationshipsbetween these critical parameters and the tool performance indicator.The system is applicable to many types of semiconductor manufacturingtools, including but not limited to plasma etch tools, atomic layerdeposition tools, and chemical vapor deposition tools. The system canalso be generalized for multiple tool performance outputs (e.g.,throughput, downtime, uptime, repair cost, etch bias, depositionthickness, particle count, sidewall angle, etc.).

FIGS. 15-16 illustrate various methodologies in accordance with one ormore embodiments of the subject application. While, for purposes ofsimplicity of explanation, the one or more methodologies shown hereinare shown and described as a series of acts, it is to be understood andappreciated that the subject innovation is not limited by the order ofacts, as some acts may, in accordance therewith, occur in a differentorder and/or concurrently with other acts from that shown and describedherein. For example, those skilled in the art will understand andappreciate that a methodology could alternatively be represented as aseries of interrelated states or events, such as in a state diagram.Moreover, not all illustrated acts may be required to implement amethodology in accordance with the innovation. Furthermore, interactiondiagram(s) may represent methodologies, or methods, in accordance withthe subject disclosure when disparate entities enact disparate portionsof the methodologies. Further yet, two or more of the disclosed examplemethods can be implemented in combination with each other, to accomplishone or more features or advantages described herein.

FIG. 15 illustrates an example methodology 1500 for modeling afunctional relationship between a tool performance indicator and a setof tool parameters of a semiconductor fabrication system. Initially, at1502, a tool performance indicator of a semiconductor fabrication systemis selected for analysis. The selected tool performance indicator canrepresent such tool performance outputs as wafer throughput, systemdowntime, system uptime, repair costs, etc. The tool performanceindicator may also be a particular tool output product characteristic,such as etch bias, deposition thickness, particle count, sidewall angle,etc.

At 1504, a set of tool parameters to be correlated with the toolperformance indicator are selected. Exemplary tool parameters caninclude metrology measurements for one or more manufactured wafers, suchas incoming CD, deposited thickness, refractive index of material, etc.The tool parameters can also include sensor readings taken during themanufacturing process (e.g., pressures, temperatures, power, gas flows,etc.) and/or tool operational performance measures (e.g., age of partson the tools, processing time, set-up times, wafer load and unloadtimes, etc.).

At 1506, the relative impact of each tool parameter on the selected toolperformance indicator is determined. The impact of a given toolparameter is a measure of how sensitive the tool performance indicatoris to changes in the given tool parameter, or a degree of influence thegiven tool parameter has over the value of the tool performanceindicator. At 1508, a subset of the tool parameters determined to havethe greatest impact on the selected tool performance indicator isidentified based on the relative impacts determined at step 1506. At1510, a functional relationship between the tool performance indicatorand the subset of tool parameters identified in step 1508 is modeled.This methodology for modeling a semiconductor tool performance indicatorcan greatly simplify analysis by reducing the number of tool parametersto a smaller set of critical parameters identified as having thegreatest influence on the selected performance indicator.

FIG. 16 illustrates an example methodology 1600 for autonomouslyidentifying and modeling tool parameter impact on a tool performancemetric. Initially, at 1602, tool parameter data and tool performancedata is received. The data can correspond to one or more process runs ofa semiconductor fabrication system. The tool parameter data can comprisemetrology data measured for one or more manufactured wafers, such asincoming CD, deposited thickness, refractive index of material, etc.Tool parameter data can also comprise sensor readings taken during themanufacturing process (e.g., pressures, temperatures, power, gas flows,etc.) and/or tool operational performance measures (e.g., age of partson the tools, processing time, set-up times, wafer load and unloadtimes, etc.). Tool performance data can comprise data relating to suchtool performance outputs as throughput, downtime, uptime, repair costs,etc. Tool performance data may also include tool output productcharacteristics, such as etch bias, deposition thickness, particlecount, sidewall angle, etc.

At 1604, the tool parameter data is separated for respective N toolparameters. At 1606 a counter i is set to 1. At 1608, a functionO=fi(Pi) is generated for an ith tool parameter characterizing arelationship between a tool performance indicator O and the toolparameter Pi. The function can be learned based on the tool parameterdata for Pi (separated out at step 1604) and the tool performance datarelating to the tool performance indicator.

At 1610, a determination is made as to whether functions have beengenerated for all N parameters. If it is determined that functions havenot been generated for all N parameters, the methodology moves to step1612, where counter i is incremented, and step 1608 is repeated for thenext tool parameter. Alternatively, if it is determined at step 1510that functions have been generated for all N parameters, the methodologymoves to step 1614.

At 1614, each function generated by steps 1608-1610 is scored based onhow well the predicted value O matches the actual tool performance dataand/or based on a sensitivity measure for the derived function. Thesensitivity measure describes a sensitivity of the tool performanceindicator to changes in the tool parameter, and can be determined basedin part on a numerical or symbolic differential calculated for eachfunction.

At 1616, the N tool parameters are ranked according to the scoresdetermined at 1614, and the M highest ranked tool parameters areidentified. These M highest ranked tool parameters represent the subsetof the total tool parameters determined to have the greatest impact onthe tool performance metric. At 1618, a new function is generated thatmodels the tool performance indicator as a function of the M highestranked parameters identified at step 1616. This new function can be usedto predict future tool performance behavior based on tool parametertrends, assist with scheduling preventative maintenance and identifyingwhere maintenance efforts should be focused, or other such applications.

The various aspects (e.g., in connection with receiving one or moreselections, determining the meaning of the one or more selections,distinguishing a selection from other actions, implementation ofselections to satisfy the request, and so forth) can employ variousartificial intelligence-based schemes for carrying out various aspectsthereof. For example, a process for determining if a particular actionis a request for an action to be performed or a general action (e.g., anaction that the user desires to perform manually) can be enabled throughan automatic classifier system and process.

A classifier is a function that maps an input attribute vector, x=(x1,x2, x3, x4, xn), to a confidence that the input belongs to a class, thatis, f(x)=confidence(class). Such classification can employ aprobabilistic and/or statistical-based analysis (e.g., factoring intothe analysis utilities and costs) to prognose or infer an action that auser desires to be automatically performed. In the case of selections,for example, attributes can be identification of a focus chamber and/ora reference chamber and the classes are criteria of the focus chamberand/or reference chamber that need to be utilized to satisfy therequest.

A support vector machine (SVM) is an example of a classifier that can beemployed. The SVM operates by finding a hypersurface in the space ofpossible inputs, which hypersurface attempts to split the triggeringcriteria from the non-triggering events. Intuitively, this makes theclassification correct for testing data that is near, but not identicalto training data. Other directed and undirected model classificationapproaches include, for example, naïve Bayes, Bayesian networks,decision trees, neural networks, fuzzy logic models, and probabilisticclassification models providing different patterns of independence canbe employed. Classification as used herein also is inclusive ofstatistical regression that is utilized to develop models of priority.

As will be readily appreciated from the subject specification, the oneor more aspects can employ classifiers that are explicitly trained(e.g., through a generic training data) as well as implicitly trained(e.g., by observing user behavior, receiving extrinsic information). Forexample, SVM's are configured through a learning or training phasewithin a classifier constructor and feature selection module. Thus, theclassifier(s) can be used to automatically learn and perform a number offunctions, including but not limited to determining according to apredetermined criteria when to compare chambers, which chambers tocompare, what chambers to group together, relationships betweenchambers, and so forth. The criteria can include, but is not limited to,similar requests, historical information, and so forth.

Referring now to FIG. 17, illustrated is a block diagram of a computeroperable to execute the disclosed aspects. In order to provideadditional context for various aspects thereof, FIG. 17 and thefollowing discussion are intended to provide a brief, generaldescription of a suitable computing environment 1700 in which thevarious aspects of the embodiment(s) can be implemented. While thedescription above is in the general context of computer-executableinstructions that may run on one or more computers, those skilled in theart will recognize that the various embodiments can be implemented incombination with other program modules and/or as a combination ofhardware and software.

Generally, program modules include routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the disclosed aspects can be practiced with other computer systemconfigurations, including single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, micro-controllers, embeddedcontrollers, multi-core processors, and the like, each of which can beoperatively coupled to one or more associated devices.

The illustrated aspects of the various embodiments may also be practicedin distributed computing environments where certain tasks are performedby remote processing devices that are linked through a communicationsnetwork. In a distributed computing environment, program modules can belocated in both local and remote memory storage devices.

Computing devices typically include a variety of media, which caninclude computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structureddata, or unstructured data. Computer-readable storage media can include,but are not limited to, RAM, ROM, EEPROM, DRAM, flash memory or othermemory technology, CD-ROM, digital versatile disk (DVD) or other opticaldisk storage, magnetic cassettes, magnetic tape, magnetic disk storageor other magnetic storage devices, or other tangible and/ornon-transitory media which can be used to store desired information.Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules, or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and includes any information deliveryor transport media. The term “modulated data signal” or signals refersto a signal that has one or more of its characteristics set or changedin such a manner as to encode information in one or more signals. By wayof example, and not limitation, communication media include wired media,such as a wired network or direct-wired connection, and wireless mediasuch as acoustic, microwave, RF, infrared and other wireless methods(e.g., IEEE 802.12X, IEEE 802.15.4).

With reference again to FIG. 17, the illustrative computing environment1700 for implementing various aspects includes a computer 1702, whichincludes a processing unit 1704, a system memory 1706 and a system bus1708. The system bus 1708 couples system components including, but notlimited to, the system memory 1706 to the processing unit 1704. Theprocessing unit 1704 can be any of various commercially availableprocessors. Dual microprocessors and other multi-processor architecturesmay also be employed as the processing unit 1704.

The system bus 1708 can be any of several types of bus structure thatmay further interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 1406includes read-only memory (ROM) 1710 and random access memory (RAM)1712. A basic input/output system (BIOS) is stored in a non-volatilememory 1710 such as ROM, EPROM, EEPROM, which BIOS contains the basicroutines that help to transfer information between elements within thecomputer 1702, such as during start-up. The RAM 1712 can also include ahigh-speed RAM such as static RAM for caching data.

The computer 1702 further includes a disk storage 1714, which caninclude an internal hard disk drive (HDD) (e.g., EIDE, SATA), whichinternal hard disk drive may also be configured for external use in asuitable chassis (not shown), a magnetic floppy disk drive (FDD), (e.g.,to read from or write to a removable diskette) and an optical disk drive(e.g., reading a CD-ROM disk or, to read from or write to other highcapacity optical media such as the DVD). The hard disk drive, magneticdisk drive and optical disk drive can be connected to the system bus1708 by a hard disk drive interface, a magnetic disk drive interface andan optical drive interface, respectively. The interface 1716 forexternal drive implementations includes at least one or both ofUniversal Serial Bus (USB) and IEEE 1094 interface technologies. Otherexternal drive connection technologies are within contemplation of thevarious embodiments described herein.

The drives and their associated computer-readable media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 1702, the drives and mediaaccommodate the storage of any data in a suitable digital format.Although the description of computer-readable media above refers to aHDD, a removable magnetic diskette, and a removable optical media suchas a CD or DVD, it should be appreciated by those skilled in the artthat other types of media which are readable by a computer, such as zipdrives, magnetic cassettes, flash memory cards, cartridges, and thelike, may also be used in the illustrative operating environment, andfurther, that any such media may contain computer-executableinstructions for performing the disclosed aspects.

A number of program modules can be stored in the drives and RAM,including an operating system 1718, one or more application programs1420, other program modules 1724, and program data 1726. All or portionsof the operating system, applications, modules, and/or data can also becached in the RAM. It is to be appreciated that the various embodimentscan be implemented with various commercially available operating systemsor combinations of operating systems.

A user can enter commands and information into the computer 1702 throughone or more wired/wireless input devices 1728, such as a keyboard and apointing device, such as a mouse. Other input devices (not shown) mayinclude a microphone, an IR remote control, a joystick, a game pad, astylus pen, touch screen, or the like. These and other input devices areoften connected to the processing unit 1704 through an input device(interface) port 1730 that is coupled to the system bus 1708, but can beconnected by other interfaces, such as a parallel port, an IEEE 1094serial port, a game port, a USB port, an IR interface, etc.

A monitor or other type of display device is also connected to thesystem bus 1708 via an output (adapter) port 1734, such as a videoadapter. In addition to the monitor, a computer typically includes otherperipheral output devices 1736, such as speakers, printers, etc.

The computer 1702 may operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 1738. The remotecomputer(s) 1738 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallyincludes many or all of the elements described relative to the computer1702, although, for purposes of brevity, only a memory/storage device1740 is illustrated.

The remote computer(s) can have a network interface 1742 that enableslogical connections to computer 1702. The logical connections includewired/wireless connectivity to a local area network (LAN) and/or largernetworks, e.g., a wide area network (WAN). Such LAN and WAN networkingenvironments are commonplace in offices and companies, and facilitateenterprise-wide computer networks, such as intranets, all of which mayconnect to a global communications network, e.g., the Internet.

When used in a LAN networking environment, the computer 1702 isconnected to the local network through a wired and/or wirelesscommunication network interface or adapter (communication connection(s))1744. The adaptor 1744 may facilitate wired or wireless communication tothe LAN, which may also include a wireless access point disposed thereonfor communicating with the wireless adaptor.

When used in a WAN networking environment, the computer 1702 can includea modem, or is connected to a communications server on the WAN, or hasother means for establishing communications over the WAN, such as by wayof the Internet. The modem, which can be internal or external and awired or wireless device, is connected to the system bus 1708 via theserial port interface. In a networked environment, program modulesdepicted relative to the computer 1702, or portions thereof, can bestored in the remote memory/storage device 1740. It will be appreciatedthat the network connections shown are illustrative and other means ofestablishing a communications link between the computers can be used.

The computer 1702 is operable to communicate with any wireless devicesor entities operatively disposed in wireless communication, e.g., aprinter, scanner, desktop and/or portable computer, portable dataassistant, communications satellite, any piece of equipment or locationassociated with a wirelessly detectable tag (e.g., a kiosk, news stand,and so forth), and telephone. This includes at least Wi-Fi andBluetooth™ wireless technologies. Thus, the communication can be apredefined structure as with a conventional network or simply an ad hoccommunication between at least two devices.

Wi-Fi, or Wireless Fidelity, allows connection to the Internet withoutwires. Wi-Fi is a wireless technology similar to that used in a cellphone that enables such devices, e.g., computers, to send and receivedata indoors and out; anywhere within the range of a base station. Wi-Finetworks use radio technologies called IEEE 802.11x (a, b, g, etc.) toprovide secure, reliable, fast wireless connectivity. A Wi-Fi networkcan be used to connect computers to each other, to the Internet, and towired networks (which use IEEE 802.3 or Ethernet).

Wi-Fi networks can operate in the unlicensed 2.4 and 5 GHz radio bands.IEEE 802.11 applies to generally to wireless LANs and provides 1 or 2Mbps transmission in the 2.4 GHz band using either frequency hoppingspread spectrum (FHSS) or direct sequence spread spectrum (DSSS). IEEE802.11a is an extension to IEEE 802.11 that applies to wireless LANs andprovides up to 54 Mbps in the 5 GHz band. IEEE 802.11a uses anorthogonal frequency division multiplexing (OFDM) encoding scheme ratherthan FHSS or DSSS. IEEE 802.11b (also referred to as 802.11 High RateDSSS or Wi-Fi) is an extension to 802.11 that applies to wireless LANsand provides 11 Mbps transmission (with a fallback to 5.5, 2 and 1 Mbps)in the 2.4 GHz band. IEEE 802.11g applies to wireless LANs and provides20+Mbps in the 2.4 GHz band. Products can contain more than one band(e.g., dual band), so the networks can provide real-world performancesimilar to the basic 10BaseT wired Ethernet networks used in manyoffices.

Referring now to FIG. 18, there is illustrated a schematic block diagramof an illustrative computing environment 1800 for processing thedisclosed architecture in accordance with another aspect. The computingenvironment 1800 includes one or more client(s) 1802. The client(s) 1802can be hardware and/or software (e.g., threads, processes, computingdevices). The client(s) 1802 can house cookie(s) and/or associatedcontextual information in connection with the various embodiments, forexample.

Computing environment 1800 also includes one or more server(s) 1804. Theserver(s) 1804 can also be hardware and/or software (e.g., threads,processes, computing devices). The servers 1804 can house threads toperform transformations in connection with the various embodiments, forexample. One possible communication between a client 1802 and servers1804 can be in the form of a data packet adapted to be transmittedbetween two or more computer processes. The data packet may include acookie and/or associated contextual information, for example. Computingenvironment 1800 includes a communication framework 1806 (e.g., a globalcommunication network such as the Internet) that can be employed tofacilitate communications between the client(s) 1802 and the server(s)1804.

Communications can be facilitated via a wired (including optical fiber)and/or wireless technology. The client(s) 1802 are operatively connectedto one or more client data store(s) 1808 that can be employed to storeinformation local to the client(s) 1802 (e.g., cookie(s) and/orassociated contextual information). Similarly, the server(s) 1804 areoperatively connected to one or more server data store(s) 1810 that canbe employed to store information local to the servers 1804.

In addition to the various embodiments described herein, it is to beunderstood that other similar embodiments can be used or modificationsand additions can be made to the described embodiment(s) for performingthe same or equivalent function of the corresponding embodiment(s)without deviating there from. Still further, multiple processing chipsor multiple devices can share the performance of one or more functionsdescribed herein, and similarly, storage can be effected across aplurality of devices. Accordingly, the invention should not be limitedto any single embodiment, but rather should be construed in breadth,spirit and scope in accordance with the appended claims.

The word “exemplary” is used herein to mean serving as an example,instance, or illustration. For the avoidance of doubt, the subjectmatter disclosed herein is not limited by such examples. In addition,any aspect or design described herein as “exemplary” is not necessarilyto be construed as preferred or advantageous over other aspects ordesigns, nor is it meant to preclude equivalent exemplary structures andtechniques known to those of ordinary skill in the art. Furthermore, tothe extent that the terms “includes,” “has,” “contains,” and othersimilar words are used, for the avoidance of doubt, such terms areintended to be inclusive in a manner similar to the term “comprising” asan open transition word without precluding any additional or otherelements.

The aforementioned systems have been described with respect tointeraction between several components. It can be appreciated that suchsystems and components can include those components or specifiedsub-components, some of the specified components or sub-components,and/or additional components, and according to various permutations andcombinations of the foregoing. Sub-components can also be implemented ascomponents communicatively coupled to other components rather thanincluded within parent components (hierarchical). Additionally, it canbe noted that one or more components may be combined into a singlecomponent providing aggregate functionality or divided into severalseparate sub-components, and that any one or more middle layers, such asa management layer, may be provided to communicatively couple to suchsub-components in order to provide integrated functionality. Anycomponents described herein may also interact with one or more othercomponents not specifically described herein but generally known bythose of skill in the art.

In view of the exemplary systems described above, methodologies that maybe implemented in accordance with the described subject matter can alsobe appreciated with reference to the flowcharts of the various figures.While for purposes of simplicity of explanation, the methodologies areshown and described as a series of blocks, it is to be understood andappreciated that the various embodiments are not limited by the order ofthe blocks, as some blocks may occur in different orders and/orconcurrently with other blocks from what is depicted and describedherein. Where non-sequential, or branched, flow is illustrated via aflowchart, it can be appreciated that various other branches, flowpaths, and orders of the blocks, may be implemented which achieve thesame or a similar result. Moreover, not all illustrated blocks may berequired to implement the methodologies described herein.

1. A system for identifying tool parameter impact on tool performance ina semiconductor fabrication system, comprising: at least onenon-transitory computer-readable medium having stored thereincomputer-executable components; and at least one processor that executesthe following computer-executable components stored on the at least onenon-transitory computer readable medium: a parameter separationcomponent configured to generate a set of functions that respectivelycharacterize a behavior of a tool performance indicator in terms of asingle tool parameter from a set of tool parameters; a ranking componentconfigured to rank the set of tool parameters according to a relativeimpact on the tool performance indicator to yield a parameter ranking,wherein the relative impact is determined based on the set of functions;and a filtering component configured to identify a subset of the toolparameters having a significant impact on the tool performance indicatorbased on the parameter ranking.
 2. The system of claim 1, furthercomprising a quality scoring component configured to determine a qualityscore for respective functions of the set of functions, wherein thequality score is a relative measure of how closely the respectivefunctions predict an actual value for the tool performance indicator. 3.The system of claim 2, wherein the ranking component is furtherconfigured to rank the set of tool parameters according to the qualityscore for the respective functions.
 4. The system of claim 1, furthercomprising a sensitivity component configured to determine a sensitivityscore for respective functions of the set of functions, wherein thesensitivity score is a measure of sensitivity of the tool performanceindicator to changes in the single tool parameter corresponding to therespective functions.
 5. The system of claim 4, wherein the rankingcomponent is further configured to rank the set of tool parametersaccording to the sensitivity score for the respective functions.
 6. Thesystem of claim 4, wherein the sensitivity component is furtherconfigured to determine the sensitivity score for the respectivefunctions based in part on a numerical or symbolic differentialdetermined for the respective functions.
 7. The system of claim 1,further comprising an interface component configured to receive inputspecifying at least one of a number of highest ranking tool parametersto maintain as the subset of the tool parameters or a number of lowestranking tool parameters to eliminate to yield the subset of the toolparameters.
 8. The system of claim 7, wherein the interface component isfurther configured to render the subset of the tool parameters on adisplay.
 9. The system of claim 1, wherein the parameter separationcomponent is further configured to generate the set of functions basedon tool parameter data and tool performance data measured for one ormore process runs of a semiconductor fabrication system.
 10. The systemof claim 1, further comprising a composite function component configuredto generate a composite function that characterizes the tool performanceindicator in terms of the subset of the tool parameters.
 11. The systemof claim 10, wherein the composite function component is furtherconfigured to iteratively update the composite function based on atleast one of new tool parameter data or new tool performance data.
 12. Amethod for determining an impact of one or more semiconductor toolparameters on a tool performance indicator, comprising: using at leastone processor executing computer-executable instructions embodied on atleast one non-transitory computer-readable medium to perform operations,the operations comprising: deriving a set of functions based on toolparameter data and tool performance data recorded for a semiconductorfabrication system, wherein the set of functions respectivelycharacterize a non-linear relationship between a tool performanceindicator and a single tool parameter of a set of tool parameters;ranking the set of tool parameters according to a degree of influencerespective tool parameters of the set of tool parameters have on thetool performance indicator, wherein the degree of influence isdetermined based on the set of functions; and identifying a subset ofthe tool parameters determined to have a critical degree of influence onthe tool performance indicator based on the ranking.
 13. The method ofclaim 11, the operations further comprising: assigning a quality scorefor respective functions of the set of functions based on adetermination of how closely an output the respective functions matchesan actual value of the tool performance indicator, wherein the rankingcomprises ranking the set of tool parameters based on the quality scorefor the respective functions.
 14. The method of claim 11, the operationsfurther comprising: assigning a sensitivity score for respectivefunctions of the set of functions based on a determination of howsensitive the tool performance indicator is to changes in the singletool parameter corresponding to the respective functions, wherein theranking comprises ranking the set of tool parameters based on thesensitivity score for the respective functions.
 15. The method of claim14, wherein the assigning the sensitivity score comprises: performing anumerical or symbolic differential on the respective functions; anddetermining the sensitivity score for the respective functions based onthe numerical or symbolic differential.
 16. The method of claim 11, theoperations further comprising deriving a composite functioncharacterizing a relationship between the tool performance indicator andthe subset of the tool parameters.
 17. The method of claim 16, theoperations further comprising iteratively updating the compositefunction in accordance with at least one of new tool parameter data ornew tool performance data.
 18. A computer-readable medium having storedthereon computer-executable instructions that, in response to executionby a system including a processor, cause the system to performoperations, the operations including: generating a set of functions thatrespectively describe a correlation between a single tool parameter, ofa set of tool parameters associated with a semiconductor fabricationsystem, and a tool performance indicator of the semiconductorfabrication system; ranking the set of tool parameters according to arelative impact respective tool parameters of the set of tool parametershave on the tool performance indicator, wherein the relative impact isdetermined based on the set of functions; and outputting a subset of thetool parameters determined to have a significant impact on the toolperformance indicator based on the ranking.
 19. The computer-readablemedium of claim 18, wherein the ranking comprises ranking the set oftool parameters based on respective quality scores assigned to the setof functions, the respective quality scores indicating how closelyrespective functions, of the set of functions, predict an actual valueof the tool performance indicator.
 20. The computer-readable medium ofclaim 18, wherein the ranking comprises ranking the set of toolparameters based on respective sensitivity scores assigned to the set offunctions, the respective sensitivity scores indicating a sensitivity ofthe tool performance indicator to changes in the single tool parameterrespectively associated with the set of functions.